The present invention relates to methods and systems for receiving direct-sequence code division multiple access signals, in general and to methods and systems for adaptively receiving such signals, in particular.
In recent years, direct-sequence (DS) code division multiple access (CDMA) spread spectrum communication systems and methods experience growing attention worldwide. The IS-95 cellular communication standard is one example for application of DS-CDMA communications, which are described in TIA/EIA/IS-95-A, xe2x80x9cMobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System,xe2x80x9d Feb. 27, 1996.
Other implementations of CDMA can be found in third generation cellular systems, wireless multimedia systems, personal satellite mobile systems, and more. The basic principle of direct sequence code division multiple access communications, is that each user is assigned with a distinct spreading code, which is often referred to as a pseudo noise (PN) sequence. The spreading code bits (also called chips), are used to modulate the user data. The number of chips used to modulate one data symbol is known as the spreading factor (processing gain) of the system, and it is related to the spreading in bandwidth between the (unmodulated) user data and the CDMA signal.
In its simplest form, the base-band equivalent of the transmitted CDMA signal is,                               T          ⁡                      [            n            ]                          =                              ∑                          i              =              1                        K                    ⁢                      xe2x80x83                    ⁢                                                    a                i                            ⁡                              [                                  ⌊                                      n                    /                    SF                                    ⌋                                ]                                      ·                                          PN                i                            ⁡                              [                n                ]                                                                        Equation  1            
where SF is the spreading factor, └n/SF┘ denotes the integer part of n/SF, ai[└n/SF┘] and PNi[n] are the data symbol and spreading code of the i-th user, respectively, and K is the number of active users. Note that by the definition of └n/SF┘, ai[└n/SF┘] is fixed for SF consecutive chips, in accordance with the definition above that each data symbol is modulated by SF chips.
If TS and TC denote the symbol and chip intervals in seconds, respectively, then TS=SFxc2x7TC. The chip rate is defined as 1/TC, and the symbol rate is defined as 1/TS. Accordingly, the chip rate is SF times greater than the symbol rate.
In a DS-CDMA system, all of the users are continuously transmitting over the same frequency band. Thus, at the receiver end, each user is distinguishable from all other users, only through his spreading code. The spreading codes are therefore designed to minimize cross-talk effects between the different users. Conventional systems often use orthogonal spreading sequences.
In practice, however, channel distortions and asynchronicity modify the transmitted signals, and as a consequence, cross-talks between the users exist even when orthogonal spreading codes are utilized by the transmitter.
A plurality of receiver structures are known in the art for DS-CDMA signals, including single-user (SU) and multi-user (MU) receivers, interference cancellation (IC) receivers, and more.
A conventional single-user receiver correlates the received signal with the spreading code of the desired user (user no. 1), as follows                                           y            1                    ⁡                      [            m            ]                          =                              1                          2              ·              SF                                ⁢                                    ∑                              n                =                1                            SF                        ⁢                          xe2x80x83                        ⁢                                          R                ⁡                                  [                                                            m                      ·                      SF                                        +                    n                                    ]                                            ·                                                                    PN                    1                                    ⁡                                      [                                                                  m                        ·                        SF                                            +                      n                                        ]                                                  *                                                                        Equation  2            
where R[n] denotes the received signal after down conversion and sampling and xe2x80x9c*xe2x80x9d denotes the complex conjugation. For simplicity we assume QPSK signaling in Equation 2. A simplistic example is provided, by setting K=2 (i.e. a system which includes two users) and discarding channel degradation (i.e. R[n]=T[n]). Hence, the following expression is obtained by substituting Equation 1 into Equation 2,
y1[m]=a1[m]+CrossCorr1,2[m]xc2x7a2[m]xe2x80x83xe2x80x83Equation 3
where                                                                                           CrossCorr                                      i                    ,                    j                                                  ⁡                                  [                  m                  ]                                            =                              xe2x80x83                            ⁢                                                1                                      2                    ·                    SF                                                  ·                                                      ∑                                          l                      =                      1                                        SF                                    ⁢                                      xe2x80x83                                    ⁢                                                                                                              PN                          i                                                ⁡                                                  [                                                                                    m                              ·                              SF                                                        +                            l                                                    ]                                                                    *                                        ·                                                                                                                                          xe2x80x83                            ⁢                                                PN                  j                                ⁡                                  [                                                            m                      ·                      SF                                        +                    l                                    ]                                                                                        Equation  4            
The term CrossCorr1,2[m]xc2x7a2[m] in Equation 3 denotes the interference caused to user 1 by user 2. This, simple example reveals a well known weakness of the SU receiver, namely, its performance are governed by the noise level induced by the cross-talk from all other channel users (see for example, A. J. Viterbi, xe2x80x9cCDMA Principals of Spread Spectrum Communicationxe2x80x9d, Addison-Wesley Publishing Company, 1995). A more advanced SU receiver includes some means of interference cancellation, which are aimed at reducing these cross-talks, and improving the receiver""s performance. For example, see the following references:
Yoshida, xe2x80x9cCDMA-AIC highly spectrum Efficient CDMA cellular system based on adaptive interference cancellationxe2x80x9d, IEICE transactions on communication v e79-b n 3 March 1996, p. 353-360,
A. Yoon, xe2x80x9cA Spread spectrum multi-access system with co-channel interference cancellationxe2x80x9d, IEEE journal of selected areas in communications, September 1993,
U.S. Pat. No. 5,105,435 to Stilwell, entitled xe2x80x9cMethod And Apparatus For Canceling Spread Spectrum Noisexe2x80x9d, and
Y. Li, xe2x80x9cSerial interference cancellation method for CDMAxe2x80x9d electronics letters, September 1994.
Multi-user (MU) receivers, jointly demodulate several or all of the received signals associated with the currently active users. The structure of MU receivers is much more complicated than that of SU receivers, but their performance are significantly better since these receivers are less sensitive to cross-talks between the users. (see for example, S. Verdu xe2x80x9cMulti-user Detectionxe2x80x9d Cambridge University Press, 1998, and the references therein).
In practice, the communication link, between the transmitter and the receiver, is often time varying. Therefore, the CDMA receiver, which can be an SU, MU or IC receiver, is required to be adaptive, thereby being capable of tracking the time variations of the communication channel. See for example U.S. Pat. No. 5,572,552 to Dent et. al, entitled xe2x80x9cMethod and system for demodulation of down-link CDMA signalsxe2x80x9d. See also, G. Woodward and B. S. Vucetic, xe2x80x9cAdaptive Detection for DS-CDMA,xe2x80x9d Proceedings of the IEEE, Vol 86, No. 7, July 1998.
Adaptive algorithms, like those available for DS-CDMA applications, are designed to minimize the expectation of a predetermined cost function (preferably a convex one) with respect to the receiver""s parameters. For example, S. Verdu, xe2x80x9cAdaptive Multi-User Detectionxe2x80x9d, Proc. IEEE Int. Symp. On Spread Spectrum Theory and Applications, (Oulu Finland, July 1994), is directed to an adaptive least-mean-squares (LMS) MU algorithm which minimizes the mean squared error between the transmitted and reconstructed symbols, i.e.
MSEixe2x89xa1E{(xc3xa2i[n]xe2x88x92ai[n])2}xe2x80x83xe2x80x83Equation 5
where xc3xa2i[n] are the MU receiver output samples at the i-th terminal, and ai[n] are the transmitted symbols of the i-th user. The cost function in Equation 5 requires training sequences. In other words, the receiver must know the exact value of at least some of the transmitted symbols (the ai[n]""s) in order to minimize this cost.
Other methods, which are known in the art, do not require training data. S. Verdu, xe2x80x9cAdaptive Multi-User Detectionxe2x80x9d, Proc. IEEE Int. Symp. On Spread Spectrum Theory Applications, (Oulu Finland, July 1994), is also directed to such a method. This method encompasses a decision directed approach, which replaces the unknown ai[n]""s by estimation values thereof.
In the binary case, for example, ai[n] accepts only two levels: xe2x80x9c1xe2x80x9d and xe2x80x9cxe2x88x921xe2x80x9d. Thus, an estimate of which can be obtained from the sign of the corresponding receiver outputs. In this case, the cost in Equation 5 reduces to
E{(xc3xa2i[n]xe2x88x92Sign{xc3xa2i[n]})2}xe2x80x83xe2x80x83Equation 6
Another method known in the art, is described in M. Honig, U Madhows and S. Verdu, xe2x80x9cBlind Adaptive Multi-User Detection, IEEE Trans. on Information Theory, July 1995. This reference is directed to a method, which is based on the fact that under certain conditions, the cost in Equation 5 is equivalent to the following cost
OEixe2x89xa1E{xc3xa2i[n]2}xe2x80x83xe2x80x83Equation 7
in the sense that the minimization of these two different cost functions yields the same receiver.
Since the criterion in Equation 7 does not involve the ai[n]""s, then there is no need for a training sequence. The cost in Equation 7 is known as the minimum output energy (MOE) cost, since the receiver is updated so that the energy at its outputs is minimized. The resulting MOE adaptive algorithms are referred to as xe2x80x9cblindxe2x80x9d multi-user algorithms, since they operate xe2x80x9cblindlyxe2x80x9d without knowing the transmitted bits.
It is often convenient to express the cost function in terms of sample averaging instead of stochastic expectations. For example, the MSE cost can be defined, at time instant as follows:                                           MSE            i                    ⁡                      (            n            )                          ≡                              ∑                          k              =              1                        n                    ⁢                      xe2x80x83                    ⁢                                                    (                                                                                                    a                        ^                                            i                                        ⁡                                          [                      k                      ]                                                        -                                      a                    ⁡                                          [                      k                      ]                                                                      )                            2                        ⁢                          λ                              (                                  n                  -                  k                                )                                                                        Equation  8            
where 0 less than xcexxe2x89xa61 is an exponential forgetting factor giving more weight to recent samples than to previous ones, thus allowing tracking capabilities.
The following references are directed to an adaptive recursive least squares (RLS) type algorithm for the minimization of this criterion:
H. V. Poor and X. Wang, xe2x80x9cCode aided interference suppression for DS/CDMA communications: Interference suppression capabilityxe2x80x9d, IEEE Tran. On Comm, September 1997.
H. V. Poor and X Wang, xe2x80x9cCode aided interference suppression for DS/CDMA communications: Parallel Blind Adaptive Implementationsxe2x80x9d, IEEE Tran. On Comm, September 1997.
Similar algorithms can be derived for the cost function in Equation 7, by re-writing it in the following form                                           OE            i                    ⁡                      (            n            )                          ≡                              ∑                          k              =              1                        n                    ⁢                      xe2x80x83                    ⁢                                                                                          a                    ^                                    i                                ⁡                                  [                  k                  ]                                            2                        ⁢                          λ                              (                                  n                  -                  k                                )                                                                        Equation  9            
Reference is now made to FIG. 1A, which is a schematic illustration of a system for adaptive detection of a DS-CDMA signal, generally referenced 80, which is known in the art. System 80 is basically a processing unit, which implements any of the above methods. The received samples y[1],y[2], . . . , y[m], are provided as input to the processor. The processor, implementing any of the above methods, calculates the adaptation parameters {circumflex over (xcex8)}[m] for minimizing the cost function which characterizes the receiver 80.
It would be obvious to someone skilled in the art, that the received samples y[1],y[2], . . . , y[m] may also be vector valued, e.g. the outputs from a bank of SU receivers each tuned to a different user.
Reference is now made to FIG. 1B which is a schematic illustration of a bank of rake receivers, known in the art. It is noted that a rake receiver is a single user (SU) receiver.
Section 50 includes an array of rake receivers 52 and a processor 56, connected thereto. The array 56 includes a plurality of rake receivers 54A, 54B, 54C and 54M, which are set to receive the signals of as much as M users.
The input samples to the processor (56) are vector valued in this case, so that each sample Y[i] is given by       Y    ⁡          [      i      ]        =      [                                                      Y              ⁡                              [                i                ]                                      1                                                                          Y              ⁡                              [                i                ]                                      2                                                ⋮                                                                Y              ⁡                              [                i                ]                                      M                                ]  
where Y[i]k is the i-th sample of the k-th rake receiver.
The embodiment in FIG. 1B is often utilized in adaptive MU receivers where the processor 56 can detect the transmitted information of user 1 by processing the samples provider by rake receiver 54A, while taking into consideration the influence of the respective samples of the second user, as provided by the second rake receiver (54B), the respective samples of the third user, as provided by the third rake receiver (54C) and so forth.
Adaptive algorithms are often conveniently described in terms of their bandwidth. An adaptive algorithm is considered to have an overall response of a low-pass filter due to the inherent averaging operation that is either implicitly or explicitly dominant in any adaptive scheme. The bandwidth of this equivalent low-pass response is considerably lower than that of the data, and it governs the tracking and noise rejection capabilities of the adaptive algorithm. A large bandwidth implies fast tracking but relatively high residual noise (i.e. large error variance of {circumflex over (xcex8)}[m]), whereas low bandwidth implies good noise rejection but poor tracking capabilities.
In many DS-CDMA systems, the spreading code is much longer than the symbol period (the down-link of IS-95 systems, for example). Adaptive algorithms, like the ones reported in the above references, whose bandwidth is lower than the symbol rate, are inappropriate for such systems. This is due to the fact that these algorithms are unable to track the fast varying interference between the users (whose bandwidth is proportional to the symbol rate since a new interference value is produced with each new data symbol). The reason for the fast varying nature of the interference lies in the fact that when the PN sequence spans more than one data symbol, different portions of which are utilized in Equation 4 with different data symbols. Thus, the cross-correlation accepts a different value with each new data symbol.
In some cases, this situation is unavoidable, (e.g. when random spreading codes are utilized). However, in most cases of practical interest, the spreading codes are non-random and finite.
It is an object of the present invention to provide a novel method for receiving a DS-CDMA signal, which overcomes the disadvantages of the prior art.
It is another object of the present invention to provide a novel DS-CDMA receiver, which overcomes the disadvantages of the prior art.
In accordance with the present invention there is provided a method for receiving DS-CDMA signal. The method is for implementing in a receiver receiving a signal, where the signal includes data which is at least modulated by one cyclic sequence. The method includes the steps of:
receiving a portion of the signal, where the portion is modulated by a predetermined section of the cyclic sequence,
receiving an additional portion of the signal, where the additional portion is modulated by the same predetermined section of the cyclic sequence,
jointly processing the portion and the additional portion, and
producing a set of receiver parameters, which minimize a predetermined cost function for the predetermined section of the cyclic sequence.
The method of the invention can also include the step of predetermining sections within the cyclic sequence. It is noted that these sections can include one or more elements of the cyclic sequence.
According to another aspect of the invention, the received signal is demodulated by the cyclic sequence, thereby extracting the data symbols which are contained therein. Then, the above operations are performed for the symbols, with respect to the predetermined sections of the cyclic sequence, where preferably, the length of these sections is in the order of a symbol.
Accordingly, the method of the present invention with respect to this aspect, includes the steps of:
demodulating the signal, by the cyclic sequence, thereby producing a plurality of received samples,
determining a plurality of sections, each the section having a length of at least one sample, each the section being demodulated by a predetermined portion of the cyclic sequence,
detecting portions of the demodulated signal, which are associated with each of the sections,
jointly processing the detected portions, which are associated by a selected one of the sections, and
producing a set of receiver parameters for each the sections, the receiver parameters minimizing a predetermined cost function for the selected section.
It is noted that the received signal can be a signal is a DS-CDMA signal or any other spread signal which is modulated by a cyclic sequence.
The demodulating and extracting of the data symbols can include rake demodulating the DS-CDMA signal, using a rake receiver.
In accordance with another aspect of the invention, there is thus provided a receiver for detecting a signal, where the signal includes data which is at least modulated by a cyclic sequence. The receiver includes a plurality of processing units, each the processing units being associated with a predetermined section of the cyclic sequence, and a distributing unit, connected to each the processing units.
The distributing unit receives the signal, detects portions of the signal, each the portions being associated with one of the predetermined sections. The distributing unit provides selected ones of the portions to a selected one of the processing units, wherein both the selected portions and the selected processing unit are associated with the same predetermined section. Each of the processing units processes the selected portions, thereby producing set of receiver parameters which minimize a predetermined cost function for that specific section.
In accordance with a further aspect of the invention there is thus provided a receiver for detecting a signal. The signal includes data which is at least modulated by a cyclic sequence. The receiver includes a despreading unit, for demodulating the signal by the cyclic sequence, thereby producing a demodulated signal, a plurality of processing units, each the processing units being associated with a predetermined section of the cyclic sequence, and a distributing unit, connected between the despreading unit and each of the processing units.
Accordingly, this receiver demodulates the received signal according to the cyclic sequence and operates on the demodulated symbols, with respect to their location, as they were modulated, within the cyclic sequence. It is noted that the despreading unit can includes a rake receiver.